Asthma exacerbation and proximity of residence to major roads: a population-based matched case-control study among the pediatric Medicaid population in Detroit, Michigan

<p>Abstract</p> <p>Background</p> <p>The relationship between asthma and traffic-related pollutants has received considerable attention. The use of individual-level exposure measures, such as residence location or proximity to emission sources, may avoid ecological biases.</p> <p>Method</p> <p>This study focused on the pediatric Medicaid population in Detroit, MI, a high-risk population for asthma-related events. A population-based matched case-control analysis was used to investigate associations between acute asthma outcomes and proximity of residence to major roads, including freeways. Asthma cases were identified as all children who made at least one asthma claim, including inpatient and emergency department visits, during the three-year study period, 2004-06. Individually matched controls were randomly selected from the rest of the Medicaid population on the basis of non-respiratory related illness. We used conditional logistic regression with distance as both categorical and continuous variables, and examined non-linear relationships with distance using polynomial splines. The conditional logistic regression models were then extended by considering multiple asthma states (based on the frequency of acute asthma outcomes) using polychotomous conditional logistic regression.</p> <p>Results</p> <p>Asthma events were associated with proximity to primary roads with an odds ratio of 0.97 (95% CI: 0.94, 0.99) for a 1 km increase in distance using conditional logistic regression, implying that asthma events are less likely as the distance between the residence and a primary road increases. Similar relationships and effect sizes were found using polychotomous conditional logistic regression. Another plausible exposure metric, a reduced form response surface model that represents atmospheric dispersion of pollutants from roads, was not associated under that exposure model.</p> <p>Conclusions</p> <p>There is moderately strong evidence of elevated risk of asthma close to major roads based on the results obtained in this population-based matched case-control study.</p

Data-driven approach for creating synthetic electronic medical records

<p>Abstract</p> <p>Background</p> <p>New algorithms for disease outbreak detection are being developed to take advantage of full electronic medical records (EMRs) that contain a wealth of patient information. However, due to privacy concerns, even anonymized EMRs cannot be shared among researchers, resulting in great difficulty in comparing the effectiveness of these algorithms. To bridge the gap between novel bio-surveillance algorithms operating on full EMRs and the lack of non-identifiable EMR data, a method for generating complete and synthetic EMRs was developed.</p> <p>Methods</p> <p>This paper describes a novel methodology for generating complete synthetic EMRs both for an outbreak illness of interest (tularemia) and for background records. The method developed has three major steps: 1) synthetic patient identity and basic information generation; 2) identification of care patterns that the synthetic patients would receive based on the information present in real EMR data for similar health problems; 3) adaptation of these care patterns to the synthetic patient population.</p> <p>Results</p> <p>We generated EMRs, including visit records, clinical activity, laboratory orders/results and radiology orders/results for 203 synthetic tularemia outbreak patients. Validation of the records by a medical expert revealed problems in 19% of the records; these were subsequently corrected. We also generated background EMRs for over 3000 patients in the 4-11 yr age group. Validation of those records by a medical expert revealed problems in fewer than 3% of these background patient EMRs and the errors were subsequently rectified.</p> <p>Conclusions</p> <p>A data-driven method was developed for generating fully synthetic EMRs. The method is general and can be applied to any data set that has similar data elements (such as laboratory and radiology orders and results, clinical activity, prescription orders). The pilot synthetic outbreak records were for tularemia but our approach may be adapted to other infectious diseases. The pilot synthetic background records were in the 4-11 year old age group. The adaptations that must be made to the algorithms to produce synthetic background EMRs for other age groups are indicated.</p

ICAR: endoscopic skull‐base surgery

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An open challenge to advance probabilistic forecasting for dengue epidemics

This is the final version. Available on open access from the National Academy of Sciences via the DOI in this recordData Availability: Data deposition: The data are available at https://github.com/cdcepi/dengue-forecasting-project-2015 (DOI: https://doi.org/10.5281/zenodo.3519270).A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue

Impact of Systemic Inflammation and Autoimmune Diseases on apoA-I and HDL Plasma Levels and Functions

The cholesterol of high-density lipoproteins (HDLs) and its major proteic component, apoA-I, have been widely investigated as potential predictors of acute cardiovascular (CV) events. In particular, HDL cholesterol levels were shown to be inversely and independently associated with the risk of acute CV diseases in different patient populations, including autoimmune and chronic inflammatory disorders. Some relevant and direct anti-inflammatory activities of HDL have been also recently identified targeting both immune and vascular cell subsets. These studies recently highlighted the improvement of HDL function (instead of circulating levels) as a promising treatment strategy to reduce inflammation and associated CV risk in several diseases, such as systemic lupus erythematosus and rheumatoid arthritis. In these diseases, anti-inflammatory treatments targeting HDL function might improve both disease activity and CV risk. In this narrative review, we will focus on the pathophysiological relevance of HDL and apoA-I levels/functions in different acute and chronic inflammatory pathophysiological conditions

Protein and cholesterol electrophoresis of plasma samples from captive cownose ray ( Rhinoptera bonasus

Our study was undertaken to assess the application of semiautomated methods available at the reference laboratory level for the evaluation of plasma protein and cholesterol via electrophoresis in samples from cownose rays (Rhinoptera bonasus). Three groups of animals were assessed: clinically normal, clinically abnormal, and parasitized with leeches. As reported previously, the albumin band was negligible; the protein electrophoretograms were dominated by a large beta-globulin fraction. While the group of samples from the leech-parasitized rays did not show any large differences, the abnormal group exhibited significantly elevated total solids and cholesterol levels. The latter was related to a significant increase in very low density lipoprotein levels. The results demonstrate the potential application of these laboratory methods in quantitation of plasma proteins and cholesterol fractions in subclass Elasmobranchii